Machine Learning and Cybersecurity
What is machine learning?
Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on teaching and developing algorithms to learn patterns from existing data. Continuous training enables machine learning models to do the following:
- Make predictions and decisions
- Identify key attributes
- Categorize information
- Generate new content
- Operate workflows intelligently
- Make decisions
Artificial intelligence vs. machine learning
How do AI and machine learning differ? AI refers to making intelligent machines that are more humanlike and capable of performing various tasks. ML is focused on creating models, such as algorithms that are explicitly programmed to perform specific tasks with data.
Think of it like this: AI is a vast toolbox filled with different tools for various tasks, and ML is one of the tools in the box. ML is like a specialized screwdriver—a powerful and adaptable tool that learns and adjusts its operation based on the given tasks.
What is machine learning in cybersecurity?
ML is essential to cybersecurity to speed up and automate the analysis of large amounts of data. There is no specific security algorithm. ML is used in cybersecurity in numerous ways, including the capability to detect malware, phishing emails, and anomalies.
ML analyzes user behavior, provides threat intelligence analysis, and ensures endpoint and network security. It also assists in managing vulnerabilities, prioritizing risks, and improving overall proactive threat response, keeping organizations ahead of cyber threats.
3 types of machine learning for cybersecurity
Machine learning encompasses various learning approaches, each tailored to specific tasks and challenges:
Supervised learning refers to ML models that are trained on a labeled data set, meaning they learn from examples with predefined outcomes. Trained on a labeled data collection of malware and benign files, ML models can classify new files in real time and identify potential threats without specific signatures.
Unsupervised learning refers to ML models in which the training data is unlabeled. This allows the models to identify patterns and relationships within the data without predefined categories. By analyzing user activity logs, unsupervised learning can identify anomalous behavior like irregular login attempts or abnormal data access, indicating potentially compromised accounts or insider threats.
Reinforcement learning, which most closely copies human learning, teaches the algorithm through trial and error by rewarding successful actions and penalizing unsuccessful ones. Models trained via reinforcement learning are helpful for application penetration testing by mimicking real-world attacker behavior to uncover vulnerabilities and strengthen defenses.
Advantages of machine learning in cybersecurity
There are numerous pragmatic benefits to using ML as a part of a robust cybersecurity strategy. Here are just a few.
Swift Data Analysis: ML can synthesize large data sets at high speeds. This is crucial for identifying and responding to potential threats in real time.
Analyst-led Support: Augmenting the capabilities of human analysts reduces the potential of errors and enhances the overall efficiency of cybersecurity operations.
Early Stage Detection and Response: ML-powered systems can detect and respond to threats early in the kill chain, minimizing the impact of cyberattacks by swiftly identifying and mitigating potential risks.
Intelligence at Scale: As security threats evolve, ML models can continuously adapt and improve based on new data and attack patterns at scale, which is crucial for large organizations with expanding attack surfaces.
Automated Tasks: ML enables your team to automate tedious tasks like log analysis, vulnerability scanning, and incident response workflows. This frees security operations personnel to focus on strategic analysis, investigations, and threat hunting.
- Reduced False Positives: Learning from past alerts and feedback, ML models can refine their detection capabilities, minimizing the number of false alarms that waste time and resources.
Use cases for machine learning in cybersecurity
The use cases for ML in cybersecurity are only going to grow. Here’s a brief but nonexhaustive list:
Preventing and Detecting DDoS Attacks: ML can identify patterns associated with DDoS attacks, enabling proactive prevention and mitigation by analyzing network traffic, identifying anomalies, and implementing real-time mitigation strategies.
Threat Detection and Classification: ML can classify and analyze malware signatures, network behavior, and system logs to aid in identifying and understanding a large amount of cyber threats.
Static File Analysis for Threat Prevention: ML assesses file features to predict and prevent potential threats, offering an additional layer of defense against malicious files.
Behavioral Analysis for Adversary Behavior Modeling: Evaluating adversary behavior in real time, ML systems can model and predict attack patterns across the entire cyber kill chain. This includes profiling adversary tactics, techniques, and procedures (TTPs) and correlating them with historical data.
Sandbox Malware Analysis for Identifying Malicious Behavior: ML can flag and classify malicious behavior and associate it with known adversaries. It does this by executing code in a controlled environment, monitoring behavior, and correlating findings with threat intelligence.
Email Monitoring and Security: ML can identify and block suspicious or malicious messages via content analysis, attachment scanning, and sender reputation assessment.
Vulnerability Management: ML can analyze vulnerability databases, system configurations, and threat intelligence to prioritize vulnerabilities by their criticality. This allows IT and security operations teams to focus on the most significant threats.
Challenges of machine learning
With all of its benefits and advantages, there are drawbacks to using ML effectively.
Lack of High-Quality Data: ML relies on quality data for effective learning. The absence of accurate and relevant data can hinder the performance of ML models. Obtaining diverse and representative data sets is crucial for training robust models.
Balancing False Positives: Striking a balance between identifying genuine threats and avoiding false positives is crucial. Overemphasis on one aspect can lead to inefficient cybersecurity practices. To avoid false positives, fine-tune algorithms, adjust thresholds, and leverage feedback loops.
Explainability and Repeatability: ML models can lack explainability, making it challenging to understand and replicate their decision-making processes. Ensuring transparency in ML models requires using interpretable algorithms, providing model explanations, and documenting decision-making processes.
Hardening Against Adversarial Attacks: Adversarial attacks involve manipulating ML models. To make ML systems resilient against such attacks, you will need to implement robust security measures, using adversarial training techniques and regularly testing models for vulnerabilities.
Optimizing for Specific Environments: ML models need to be tailored to specific environments to achieve optimal performance. Generalization across different environments includes customizing models for specific network configurations, system architectures, and threat landscapes.
Mitigating Social Engineering Risks: ML systems may struggle to identify and mitigate risks associated with social engineering. This emphasizes the importance of human awareness in cybersecurity. Combating social engineering involves user education, awareness programs, and integrating human insights into threat analysis.
Avoiding Overfitting/Underfitting: Balancing the complexity of ML models to prevent overfitting (fitting the training data too closely) or underfitting (lack of model complexity) is essential for effective cybersecurity.
Combating attackers’ use of ML: Perpetrators use ML to optimize phishing campaigns. They also use it to automate and refine malware that evolves to outsmart conventional detection methods. To stay ahead of ML-driven threats, implement advanced anomaly detection, behavior analysis, and real-time monitoring.
Trellix’s approach to machine learning
Trellix has been leveraging AI and ML for over a decade to strengthen our protection, detection, investigation, and remediation actions. Our Trellix ReputationDB (database) is one of the largest MSSQL databases in the world. This massive collection of file and certificate reputations directly informs the efficacy of our product detections. We have more data to make more robust models.
At Trellix, we aim to blend human expertise with the ever-evolving power of ML. ML is a force multiplier for security operations teams. Leveraging AI and ML, Trellix products streamline security operations with workflow automation, advanced detections, event correlations, risk assessments, malware and code analysis, auto-generated investigative and response playbooks, and unified product knowledge across the ecosystem.
Trellix ML capabilities
Trellix native controls and Helix Connect utilize highly trained ML models. With over a decade of training our ML models provide more accurate detections, speeding up the time to detect and accelerating the time to respond, ensuring that investigations begin with the most precise initiation points. Our capabilities give analysts the context and tools to be as effective and efficient as possible from the outset.
Here’s how Trellix currently uses ML:
- Trellix leverages ML to analyze extensive security data from over a billion sensors<.li>
- 33 ML models across endpoint, email, network, and sandbox products
- 11 ML models to detect phishing based on screenshots, web page content, URL, and email metadata
- 15 ML models that provide detection of malicious PE files, VB script, and PowerShell, using both static and dynamic properties
- 24M+ endpoints leveraging machine learning protection (MLP) in production
- 150 heuristic rules, which run in addition to ML models
- 250M queries per day to MLP cloud infrastructure from endpoints
Trellix has used ML to find over 2K zero-day detections per day for files that were not previously known to be malicious, underscoring the accuracy and efficacy of our models.